Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning

Ken Orimoto; Tomoko Tateyama; Masashi Honda; Takumi Miyamoto; Shimpei Matsumoto

doi:10.1109/IIAI-AAI50415.2020.00125

Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning

Ken Orimoto, Tomoko Tateyama, Masashi Honda, Takumi Miyamoto, Shimpei Matsumoto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Tame-map is a smartphone application that allows users to easily share regional events information within their daily lives by uploading digital flyer images on the web. Although there are a lot of event flyer images uploaded daily, these data have been organized manually. This manual work is hard and takes a huge time, so a system to automate the manual work has been required. An automatic processing method will support the organization, such as categorization, assigning keywords to each image. To develop a basis of automatic flyer images processing, this paper examines detection character region in event flyer images based on machine learning. Concretely, we compared the two features which are obtained vectors based on unsupervised/supervised leanings, such as k-means and SVM respectively.

Original language	English
Title of host publication	Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020
Editors	Tokuro Matsuo, Kunihiko Takamatsu, Yuichi Ono, Sachio Hirokawa
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	602-607
Number of pages	6
ISBN (Electronic)	9781728173979
DOIs	https://doi.org/10.1109/IIAI-AAI50415.2020.00125
Publication status	Published - 09-2020
Externally published	Yes
Event	9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020 - Kitakyushu, Japan Duration: 01-09-2020 → 15-09-2020

Publication series

Name	Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

Conference

Conference	9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020
Country/Territory	Japan
City	Kitakyushu
Period	01-09-20 → 15-09-20

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems
Information Systems and Management

Access to Document

10.1109/IIAI-AAI50415.2020.00125

Cite this

Orimoto, K., Tateyama, T., Honda, M., Miyamoto, T., & Matsumoto, S. (2020). Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning. In T. Matsuo, K. Takamatsu, Y. Ono, & S. Hirokawa (Eds.), Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020 (pp. 602-607). Article 9430422 (Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI50415.2020.00125

Orimoto, Ken ; Tateyama, Tomoko ; Honda, Masashi et al. / Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning. Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020. editor / Tokuro Matsuo ; Kunihiko Takamatsu ; Yuichi Ono ; Sachio Hirokawa. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 602-607 (Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020).

@inproceedings{c60620c9dd514af5a3a1f0219f9dc487,

title = "Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning",

abstract = "Tame-map is a smartphone application that allows users to easily share regional events information within their daily lives by uploading digital flyer images on the web. Although there are a lot of event flyer images uploaded daily, these data have been organized manually. This manual work is hard and takes a huge time, so a system to automate the manual work has been required. An automatic processing method will support the organization, such as categorization, assigning keywords to each image. To develop a basis of automatic flyer images processing, this paper examines detection character region in event flyer images based on machine learning. Concretely, we compared the two features which are obtained vectors based on unsupervised/supervised leanings, such as k-means and SVM respectively.",

author = "Ken Orimoto and Tomoko Tateyama and Masashi Honda and Takumi Miyamoto and Shimpei Matsumoto",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020 ; Conference date: 01-09-2020 Through 15-09-2020",

year = "2020",

month = sep,

doi = "10.1109/IIAI-AAI50415.2020.00125",

language = "English",

series = "Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "602--607",

editor = "Tokuro Matsuo and Kunihiko Takamatsu and Yuichi Ono and Sachio Hirokawa",

booktitle = "Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020",

address = "United States",

}

Orimoto, K, Tateyama, T, Honda, M, Miyamoto, T & Matsumoto, S 2020, Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning. in T Matsuo, K Takamatsu, Y Ono & S Hirokawa (eds), Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020., 9430422, Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020, Institute of Electrical and Electronics Engineers Inc., pp. 602-607, 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020, Kitakyushu, Japan, 01-09-20. https://doi.org/10.1109/IIAI-AAI50415.2020.00125

Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning. / Orimoto, Ken; Tateyama, Tomoko; Honda, Masashi et al.
Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020. ed. / Tokuro Matsuo; Kunihiko Takamatsu; Yuichi Ono; Sachio Hirokawa. Institute of Electrical and Electronics Engineers Inc., 2020. p. 602-607 9430422 (Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning

AU - Orimoto, Ken

AU - Tateyama, Tomoko

AU - Honda, Masashi

AU - Miyamoto, Takumi

AU - Matsumoto, Shimpei

PY - 2020/9

Y1 - 2020/9

N2 - Tame-map is a smartphone application that allows users to easily share regional events information within their daily lives by uploading digital flyer images on the web. Although there are a lot of event flyer images uploaded daily, these data have been organized manually. This manual work is hard and takes a huge time, so a system to automate the manual work has been required. An automatic processing method will support the organization, such as categorization, assigning keywords to each image. To develop a basis of automatic flyer images processing, this paper examines detection character region in event flyer images based on machine learning. Concretely, we compared the two features which are obtained vectors based on unsupervised/supervised leanings, such as k-means and SVM respectively.

AB - Tame-map is a smartphone application that allows users to easily share regional events information within their daily lives by uploading digital flyer images on the web. Although there are a lot of event flyer images uploaded daily, these data have been organized manually. This manual work is hard and takes a huge time, so a system to automate the manual work has been required. An automatic processing method will support the organization, such as categorization, assigning keywords to each image. To develop a basis of automatic flyer images processing, this paper examines detection character region in event flyer images based on machine learning. Concretely, we compared the two features which are obtained vectors based on unsupervised/supervised leanings, such as k-means and SVM respectively.

UR - http://www.scopus.com/inward/record.url?scp=85107183130&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85107183130&partnerID=8YFLogxK

U2 - 10.1109/IIAI-AAI50415.2020.00125

DO - 10.1109/IIAI-AAI50415.2020.00125

M3 - Conference contribution

AN - SCOPUS:85107183130

T3 - Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

SP - 602

EP - 607

BT - Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

A2 - Matsuo, Tokuro

A2 - Takamatsu, Kunihiko

A2 - Ono, Yuichi

A2 - Hirokawa, Sachio

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

Y2 - 1 September 2020 through 15 September 2020

ER -

Orimoto K, Tateyama T, Honda M, Miyamoto T, Matsumoto S. Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning. In Matsuo T, Takamatsu K, Ono Y, Hirokawa S, editors, Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 602-607. 9430422. (Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020). doi: 10.1109/IIAI-AAI50415.2020.00125

Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this